Robotics, video games, environmental mapping and medical are some of the fields that use 3D data processing. In this paper we propose a novel optimization approach for the open source Point Cloud Library (PCL) that is frequently used for processing 3D data. Three main aspects of the PCL are discussed: point cloud creation from disparity of color image pairs; voxel grid downsample filtering to simplify point clouds; and passthrough filtering to adjust the size of the point cloud. Additionally, OpenGL shader based rendering is examined. An optimization technique based on CPU cycle measurement is proposed and applied in order to optimize those parts of the pre-processing chain where measured performance is slowest. Results show that with optimized modules the performance of the pre-processing chain has increased 69 fold.
Cloud computing is a realized wonder. It delights its users by providing applications, platforms and infrastructure without any initial investment. The “pay as you use” strategy comforts the users. The usage can be increased by adding infrastructure, tools or applications to the existing application. The realistic beauty of cloud computing is that there is no need for any sophisticated tool for access, web browser or even smartphone will do. Cloud computing is a windfall for small organizations having less sensitive information. But for large organizations, the risks related to security may be daunting. Necessary steps have to be taken for managing the issues like confidentiality, integrity, privacy, availability and so on. In this paper availability is taken and studied in a multi-dimensional perspective. Availability is taken a key issue and the mechanisms that enable enhancement are analyzed.
Efficient Point Cloud Pre-processing using The Point Cloud LibraryCSCJournals
Robotics, video games, environmental mapping and medical are some of the fields that use 3D data processing. In this paper we propose a novel optimization approach for the open source Point Cloud Library (PCL) that is frequently used for processing 3D data. Three main aspects of the PCL are discussed: point cloud creation from disparity of color image pairs; voxel grid downsample filtering to simplify point clouds; and passthrough filtering to adjust the size of the point cloud. Additionally, OpenGL shader based rendering is examined. An optimization technique based on CPU cycle measurement is proposed and applied in order to optimize those parts of the pre-processing chain where measured performance is slowest. Results show that with optimized modules the performance of the pre-processing chain has increased 69 fold.
Scheduling in Virtual Infrastructure for High-Throughput Computing IJCSEA Journal
This document summarizes a study on improving the efficiency of resource utilization in virtual infrastructure for high-throughput computing. The study proposes a pre-staging model where virtual machine images are pre-loaded on execution nodes and jobs are directly submitted to the virtual machines. Experimental results show that the pre-staging model improves job execution times by 10-15 times compared to using Condor's virtual universe, with greater improvements for non-HPC jobs. The overhead of virtualization also reduces performance gains for HPC jobs like MPI applications.
This document discusses performance analysis of cloud computing services. It begins by defining cloud computing and describing its key characteristics like on-demand access to computing resources and pay-per-use models. It then reviews several studies on using virtualization technologies and frameworks for evaluating cloud performance and workload generation. The document concludes that tools are needed for comprehensive performance analysis of large scientific clouds to evaluate metrics like response time, cost and scalability across different cloud vendors.
IJERD (www.ijerd.com) International Journal of Engineering Research and Devel...IJERD Editor
This document summarizes and compares three computing models: cluster computing, grid computing, and cloud computing. Cluster computing involves linking together multiple computers to work as a single system for high performance computing tasks. Grid computing divides and distributes large programs across interconnected computers. Cloud computing provides on-demand access to shared computing resources over the internet. The document discusses challenges, examples of projects and applications for each model to provide an overview of how they differ and are applied.
IRJET- Load Balancing and Crash Management in IoT EnvironmentIRJET Journal
This document proposes a system to provide load balancing and crash management in an Internet of Things (IoT) environment. It introduces an Application Delivery Controller (ADC) that sits between devices and data centers. The ADC monitors the load and availability of data centers using a performance counter algorithm. It routes traffic to less busy data centers using the MQTT protocol if load increases or a data center crashes. This provides uninterrupted connectivity and prevents the whole system from going down during network failures or crashes. The system was implemented with clients that can request services or publish information to servers, which acknowledge tasks using a unique machine ID for future connections.
Cloud colonography distributed medical testbed over cloudVenkat Projects
The document proposes Cloud Colonography, a cloud computing platform that handles large databases from Computed Tomographic Colonography screening tests across multiple hospitals. It analyzes these databases using Associated Multiple Databases, which achieves high classification accuracy. Tests were run on private and public cloud environments. The public cloud had improved computation times compared to private cloud, showing Cloud Colonography's potential as a new healthcare service utilizing cloud computing.
The document discusses grid computing in remote sensing data processing. It describes how grid computing can help process huge amounts of remote sensing data in real-time by distributing processing across networked computers. Key requirements for a grid environment for remote sensing include sharing computational and software resources, managing resources, and supporting different data formats. Case studies demonstrate how grid middleware can improve the efficiency of tasks like image deblurring and generating lookup tables for aerosol analysis.
Cloud computing is a realized wonder. It delights its users by providing applications, platforms and infrastructure without any initial investment. The “pay as you use” strategy comforts the users. The usage can be increased by adding infrastructure, tools or applications to the existing application. The realistic beauty of cloud computing is that there is no need for any sophisticated tool for access, web browser or even smartphone will do. Cloud computing is a windfall for small organizations having less sensitive information. But for large organizations, the risks related to security may be daunting. Necessary steps have to be taken for managing the issues like confidentiality, integrity, privacy, availability and so on. In this paper availability is taken and studied in a multi-dimensional perspective. Availability is taken a key issue and the mechanisms that enable enhancement are analyzed.
Efficient Point Cloud Pre-processing using The Point Cloud LibraryCSCJournals
Robotics, video games, environmental mapping and medical are some of the fields that use 3D data processing. In this paper we propose a novel optimization approach for the open source Point Cloud Library (PCL) that is frequently used for processing 3D data. Three main aspects of the PCL are discussed: point cloud creation from disparity of color image pairs; voxel grid downsample filtering to simplify point clouds; and passthrough filtering to adjust the size of the point cloud. Additionally, OpenGL shader based rendering is examined. An optimization technique based on CPU cycle measurement is proposed and applied in order to optimize those parts of the pre-processing chain where measured performance is slowest. Results show that with optimized modules the performance of the pre-processing chain has increased 69 fold.
Scheduling in Virtual Infrastructure for High-Throughput Computing IJCSEA Journal
This document summarizes a study on improving the efficiency of resource utilization in virtual infrastructure for high-throughput computing. The study proposes a pre-staging model where virtual machine images are pre-loaded on execution nodes and jobs are directly submitted to the virtual machines. Experimental results show that the pre-staging model improves job execution times by 10-15 times compared to using Condor's virtual universe, with greater improvements for non-HPC jobs. The overhead of virtualization also reduces performance gains for HPC jobs like MPI applications.
This document discusses performance analysis of cloud computing services. It begins by defining cloud computing and describing its key characteristics like on-demand access to computing resources and pay-per-use models. It then reviews several studies on using virtualization technologies and frameworks for evaluating cloud performance and workload generation. The document concludes that tools are needed for comprehensive performance analysis of large scientific clouds to evaluate metrics like response time, cost and scalability across different cloud vendors.
IJERD (www.ijerd.com) International Journal of Engineering Research and Devel...IJERD Editor
This document summarizes and compares three computing models: cluster computing, grid computing, and cloud computing. Cluster computing involves linking together multiple computers to work as a single system for high performance computing tasks. Grid computing divides and distributes large programs across interconnected computers. Cloud computing provides on-demand access to shared computing resources over the internet. The document discusses challenges, examples of projects and applications for each model to provide an overview of how they differ and are applied.
IRJET- Load Balancing and Crash Management in IoT EnvironmentIRJET Journal
This document proposes a system to provide load balancing and crash management in an Internet of Things (IoT) environment. It introduces an Application Delivery Controller (ADC) that sits between devices and data centers. The ADC monitors the load and availability of data centers using a performance counter algorithm. It routes traffic to less busy data centers using the MQTT protocol if load increases or a data center crashes. This provides uninterrupted connectivity and prevents the whole system from going down during network failures or crashes. The system was implemented with clients that can request services or publish information to servers, which acknowledge tasks using a unique machine ID for future connections.
Cloud colonography distributed medical testbed over cloudVenkat Projects
The document proposes Cloud Colonography, a cloud computing platform that handles large databases from Computed Tomographic Colonography screening tests across multiple hospitals. It analyzes these databases using Associated Multiple Databases, which achieves high classification accuracy. Tests were run on private and public cloud environments. The public cloud had improved computation times compared to private cloud, showing Cloud Colonography's potential as a new healthcare service utilizing cloud computing.
The document discusses grid computing in remote sensing data processing. It describes how grid computing can help process huge amounts of remote sensing data in real-time by distributing processing across networked computers. Key requirements for a grid environment for remote sensing include sharing computational and software resources, managing resources, and supporting different data formats. Case studies demonstrate how grid middleware can improve the efficiency of tasks like image deblurring and generating lookup tables for aerosol analysis.
A Review: Metaheuristic Technique in Cloud ComputingIRJET Journal
This document reviews various meta-heuristic techniques that have been applied to problems in cloud computing, such as task scheduling, load balancing, ant colony optimization (ACO), particle swarm optimization (PSO), and gravitational search algorithm (GSA). It first provides background on cloud computing and defines common cloud computing concepts. It then surveys literature applying meta-heuristics like ACO, GA, PSO, and GSA to solve problems related to load balancing and scheduling in cloud environments. The document concludes that meta-heuristic techniques are effective for optimizing resource utilization and management in cloud computing systems.
LOCALITY SIM: CLOUD SIMULATOR WITH DATA LOCALITY ijccsa
Cloud Computing (CC) is a model for enabling on-demand access to a shared pool of configurable computing resources. Testing and evaluating the performance of the cloud environment for allocating, provisioning, scheduling, and data allocation policy have great attention to be achieved. Therefore, using cloud simulator would save time and money, and provide a flexible environment to evaluate new research
work. Unfortunately, the current simulators (e.g., CloudSim, NetworkCloudSim, GreenCloud, etc..) deal with the data as for size only without any consideration about the data allocation policy and locality. On the other hand, the NetworkCloudSim simulator is considered one of the most common used simulators because it includes different modules which support needed functions to a simulated cloud environment,
and it could be extended to include new extra modules. According to work in this paper, the NetworkCloudSim simulator has been extended and modified to support data locality. The modified simulator is called LocalitySim. The accuracy of the proposed LocalitySim simulator has been proved by building a mathematical model. Also, the proposed simulator has been used to test the performance of the
three-tire data center as a case study with considering the data locality feature
Ijarcce9 b a anjan a comparative analysis grid cluster and cloud computingHarsh Parashar
1) The document compares and contrasts three computing technologies: cluster computing, grid computing, and cloud computing.
2) Cluster computing involves connecting multiple nodes together to function as a single entity for improved performance and fault tolerance. Grid computing shares resources from multiple geographically dispersed locations.
3) Cloud computing provides on-demand access to dynamically scalable virtual resources as a utility over the Internet. It has advantages like cost savings, flexibility, and reliability.
This document discusses architectural and security management for grid computing. It begins by defining grid computing as an environment that enables sharing of distributed resources across organizations to achieve common goals. It then describes the key components of a grid, including computation resources, storage, communications, software/licenses, and special equipment. The document outlines a four-level grid architecture including a fabric level, core middleware level, user middleware level, and application level. It also discusses important aspects of grid computing such as resource balancing, reliability through distribution, parallel CPU capacity, and management of different projects. Finally, it emphasizes that security is a major concern for grid computing due to the open nature of sharing resources across organizational boundaries.
Performance Improvement of Cloud Computing Data Centers Using Energy Efficien...IJAEMSJORNAL
Cloud computing is a technology that provides a platform for the sharing of resources such as software, infrastructure, application and other information. It brings a revolution in Information Technology industry by offering on-demand of resources. Clouds are basically virtualized datacenters and applications offered as services. Data center hosts hundreds or thousands of servers which comprised of software and hardware to respond the client request. A large amount of energy requires to perform the operation.. Cloud Computing is facing lot of challenges like Security of Data, Consumption of energy, Server Consolidation, etc. The research work focuses on the study of task scheduling management in a cloud environment. The main goal is to improve the performance (resource utilization and redeem the consumption of energy) in data centers. Energy-efficient scheduling of workloads helps to redeem the consumption of energy in data centers, thus helps in better usage of resource. This is further reducing operational costs and provides benefits to the clients and also to cloud service provider. In this abstract of paper, the task scheduling in data centers have been compared. Cloudsim a toolkit for modeling and simulation of cloud computing environment has been used to implement and demonstrate the experimental results. The results aimed at analyzing the energy consumed in data centers and shows that by having reduce the consumption of energy the cloud productivity can be improved.
An Efficient Cloud Scheduling Algorithm for the Conservation of Energy throug...IJECEIAES
Method of broadcasting is the well known operation that is used for providing support to different computing protocols in cloud computing. Attaining energy efficiency is one of the prominent challenges, that is quite significant in the scheduling process that is used in cloud computing as, there are fixed limits that have to be met by the system. In this research paper, we are particularly focusing on the cloud server maintenance and scheduling process and to do so, we are using the interactive broadcasting energy efficient computing technique along with the cloud computing server. Additionally, the remote host machines used for cloud services are dissipating more power and with that they are consuming more and more energy. The effect of the power consumption is one of the main factors for determining the cost of the computing resources. With the idea of using the avoidance technology for assigning the data center resources that dynamically depend on the application demands and supports the cloud computing with the optimization of the servers in use.
A Review on Scheduling in Cloud Computingijujournal
This document reviews scheduling techniques in cloud computing. It discusses key concepts like virtualization and different scheduling algorithms. The review surveys various scheduling algorithms for tasks, workflows, real-time applications and energy efficiency. It analyzes algorithms based on parameters like makespan, cost, energy consumption and concludes many algorithms can improve resource utilization and performance while reducing energy costs.
Data Division in Cloud for Secured Data Storage using RSA AlgorithmIRJET Journal
This document proposes a method for secure data storage in the cloud using RSA encryption and data division. User data is first encrypted using RSA encryption. It is then divided into blocks and distributed across multiple cloud servers. Verification tokens are also generated before distribution to allow checking of data integrity stored on the cloud servers. If tokens from the user and cloud servers match, the data integrity is verified. If not, it indicates unauthorized modification of data by someone other than the owner. This approach aims to provide secure storage of user data in the cloud.
Welcome to International Journal of Engineering Research and Development (IJERD)IJERD Editor
This document summarizes research on techniques for virtual machine (VM) scheduling and management to improve energy efficiency in cloud computing. It discusses how VM scheduling algorithms aim to optimally map VMs to physical servers while minimizing costs and power consumption. Precopy and postcopy live migration techniques are described for managing VMs. The document surveys various algorithms for VM scheduling, including ones based on data transfer time, linear programming, and combinatorial optimization. It also discusses factors that affect VM migration efficiency such as hypervisor options and network configuration. Overall, the document provides an overview of energy-efficient approaches for VM scheduling and management in cloud computing.
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
The papers for publication in The International Journal of Engineering& Science are selected through rigorous peer reviews to ensure originality, timeliness, relevance, and readability.
An optimized scientific workflow scheduling in cloud computingDIGVIJAY SHINDE
The document discusses optimizing scientific workflow scheduling in cloud computing. It begins with definitions of workflow and cloud computing. Workflow is a group of repeatable dependent tasks, while cloud computing provides applications and hardware resources over the Internet. There are three cloud service models: SaaS, PaaS, and IaaS. The document explores how to efficiently schedule workflows in the cloud to reduce makespan, cost, and energy consumption. It reviews different scheduling algorithms like FCFS, genetic algorithms, and discusses optimizing objectives like time and cost. The document provides a literature review comparing various workflow scheduling methods and algorithms. It concludes with discussing open issues and directions for future work in optimizing workflow scheduling for cloud computing.
AUTO RESOURCE MANAGEMENT TO ENHANCE RELIABILITY AND ENERGY CONSUMPTION IN HET...IJCNCJournal
This document summarizes an article from the International Journal of Computer Networks & Communications that proposes an Auto Resource Management (ARM) scheme to improve reliability and reduce energy consumption in heterogeneous cloud computing environments. The ARM scheme includes three components: 1) static and dynamic thresholds to detect host over/underutilization, 2) a virtual machine selection policy, and 3) a method to select placement hosts for migrated VMs. It also proposes a Short Prediction Resource Utilization method to improve decision making by considering predicted future utilization along with current utilization. The scheme is tested on a cloud simulator using real workload trace data, and results show it can enhance decision making, reduce energy consumption and SLA violations.
1. Nebula is NASA's open source cloud computing platform built using OpenStack that provides on-demand access to computing resources and storage for large datasets.
2. It allows NASA researchers to run computationally intensive tasks in virtual machines and store huge datasets over 100 terabytes in size.
3. The document discusses Nebula's architecture, services, and case studies of its use at various NASA research centers to support activities like processing images from Mars missions.
An Enhanced trusted Image Storing and Retrieval Framework in Cloud Data Stora...IJERA Editor
Today’s image capturing technologies are producing High Definitional-scale images which are also heavier on memory, which has prompted many users into cloud storage, cloud computing is an service based technology and one of the cloud service is Data Storage as a Service (DSaaS), two parties are involved in this service the Cloud Service Provider and The User, user stores his vital data onto the cloud via internet example: Dropbox. but a bigger question is on trustiness over the CSP by user as user data is stored remote devices which user has no clue about, in such situation CSP has to create a trust worthiness to the costumer or user, in these paper we addressed the mention insecurity issue with a well defined trusted image Storing and retrieval framework (TISR) using compress sensing methodology.
Multicloud Deployment of Computing Clusters for Loosely Coupled Multi Task C...IOSR Journals
This document discusses deploying a computing cluster across multiple cloud providers (Amazon EC2, Elastic Hosts) for loosely coupled multi-task computing applications. It presents an experimental framework using a local data center and three cloud sites. Nine cluster configurations with varying numbers of nodes from each site are evaluated. Performance is analyzed by measuring throughput as jobs/second. Results show hybrid configurations scale linearly and have similar performance to single-site configurations. Cost is also analyzed per job, showing hybrid and local-only configurations have lower cost than cloud-only configurations. A performance-cost analysis indicates for large organizations, a local data center with cloud supplementation can be more cost effective than cloud-only configurations.
This document discusses scheduling in cloud computing. It proposes a priority-based scheduling protocol to improve resource utilization, server performance, and minimize makespan. The protocol assigns priorities to jobs, allocates jobs to processors based on completion time, and processes jobs in parallel queues to efficiently schedule jobs in cloud computing. Future work includes analyzing time complexity and completion times through simulation to validate the protocol's efficiency.
Virtual Machine Allocation Policy in Cloud Computing Environment using CloudSim IJECEIAES
This document discusses virtual machine allocation policies in cloud computing environments using the CloudSim simulation tool. It begins with an introduction to cloud computing and discusses challenges related to resource management and energy consumption. It then reviews previous research on modeling approaches, energy optimization techniques, and network topologies. A UML class model is presented for analyzing energy consumption when accessing cloud servers arranged in a step network topology. The methodology section outlines how energy consumption by system components like processors, RAM, hard disks, and motherboards will be calculated. Simulation results will depict response times and cost details for different data center configurations and allocation policies.
Improving Cloud Performance through Performance Based Load Balancing ApproachIRJET Journal
The document proposes a performance-based load balancing approach to improve cloud computing performance through load balancing and fault tolerance. It considers success ratio and past load data when distributing tasks among nodes. A fault handler is used to detect and recover from faults reactively. When a fault occurs, the handler updates node records, restarts servers, or transfers pending tasks. Task outcomes are evaluated based on status and deadlines. Nodes with successful outcomes have their success ratios incremented, while unsuccessful nodes have ratios decremented or fault handling triggered. The approach aims to map tasks to nodes with higher success ratios and lower current loads to improve quality of service. Cloudsim simulations show how success ratios for sample nodes change with this approach over multiple task assignments.
Achieving High Performance Distributed System: Using Grid, Cluster and Cloud ...IJERA Editor
To increase the efficiency of any task, we require a system that would provide high performance along with flexibilities and cost efficiencies for user. Distributed computing, as we are all aware, has become very popular over the past decade. Distributed computing has three major types, namely, cluster, grid and cloud. In order to develop a high performance distributed system, we need to utilize all the above mentioned three types of computing. In this paper, we shall first have an introduction of all the three types of distributed computing. Subsequently examining them we shall explore trends in computing and green sustainable computing to enhance the performance of a distributed system. Finally presenting the future scope, we conclude the paper suggesting a path to achieve a Green high performance distributed system using cluster, grid and cloud computing.
A Novel Approach for Workload Optimization and Improving Security in Cloud Co...IOSR Journals
This document proposes a cloud infrastructure that combines on-demand allocation of resources with opportunistic provisioning of idle cloud nodes to improve utilization. It discusses using Hadoop configuration with a Map-Reduce file splitting algorithm to distribute large files across nodes for processing. Encryption is also used to secure data during transmission and storage using an RSA algorithm. The goal is to improve CPU and storage utilization, handle large data faster by using idle nodes, and maintain security of data and services in the cloud.
6 staffing system and retention managementPreeti Bhaskar
This document discusses staffing, turnover, retention, and downsizing. It begins by defining types of turnover like voluntary, involuntary, discharge, and downsizing. Voluntary turnover can be avoidable or unavoidable. Causes of turnover include perceptions of desirability and ease of leaving a job as well as available alternatives. The document then discusses measuring turnover, analyzing reasons for leaving, and estimating costs and benefits. It provides guidelines for increasing retention through intrinsic and extrinsic rewards. Finally, it outlines retention initiatives for discharge situations and downsizing, including progressive discipline, alternatives to layoffs, and supporting employees who remain after downsizing.
The document discusses techniques for training killer whales at SeaWorld. It explains that trainers build trust with the whales by showing them love and care. They reward positive behaviors with food and praise immediately after the whales perform desired actions. When mistakes occur, trainers redirect the whales' energy to a different task rather than punishing them. Young whales are taught new tricks gradually by first rewarding them for closer approximations until they fully perform the behavior. Some key lessons for motivating people at work are to accentuate the positive, redirect mistakes, use praise immediately after good performance, and start training incrementally toward larger goals.
A Review: Metaheuristic Technique in Cloud ComputingIRJET Journal
This document reviews various meta-heuristic techniques that have been applied to problems in cloud computing, such as task scheduling, load balancing, ant colony optimization (ACO), particle swarm optimization (PSO), and gravitational search algorithm (GSA). It first provides background on cloud computing and defines common cloud computing concepts. It then surveys literature applying meta-heuristics like ACO, GA, PSO, and GSA to solve problems related to load balancing and scheduling in cloud environments. The document concludes that meta-heuristic techniques are effective for optimizing resource utilization and management in cloud computing systems.
LOCALITY SIM: CLOUD SIMULATOR WITH DATA LOCALITY ijccsa
Cloud Computing (CC) is a model for enabling on-demand access to a shared pool of configurable computing resources. Testing and evaluating the performance of the cloud environment for allocating, provisioning, scheduling, and data allocation policy have great attention to be achieved. Therefore, using cloud simulator would save time and money, and provide a flexible environment to evaluate new research
work. Unfortunately, the current simulators (e.g., CloudSim, NetworkCloudSim, GreenCloud, etc..) deal with the data as for size only without any consideration about the data allocation policy and locality. On the other hand, the NetworkCloudSim simulator is considered one of the most common used simulators because it includes different modules which support needed functions to a simulated cloud environment,
and it could be extended to include new extra modules. According to work in this paper, the NetworkCloudSim simulator has been extended and modified to support data locality. The modified simulator is called LocalitySim. The accuracy of the proposed LocalitySim simulator has been proved by building a mathematical model. Also, the proposed simulator has been used to test the performance of the
three-tire data center as a case study with considering the data locality feature
Ijarcce9 b a anjan a comparative analysis grid cluster and cloud computingHarsh Parashar
1) The document compares and contrasts three computing technologies: cluster computing, grid computing, and cloud computing.
2) Cluster computing involves connecting multiple nodes together to function as a single entity for improved performance and fault tolerance. Grid computing shares resources from multiple geographically dispersed locations.
3) Cloud computing provides on-demand access to dynamically scalable virtual resources as a utility over the Internet. It has advantages like cost savings, flexibility, and reliability.
This document discusses architectural and security management for grid computing. It begins by defining grid computing as an environment that enables sharing of distributed resources across organizations to achieve common goals. It then describes the key components of a grid, including computation resources, storage, communications, software/licenses, and special equipment. The document outlines a four-level grid architecture including a fabric level, core middleware level, user middleware level, and application level. It also discusses important aspects of grid computing such as resource balancing, reliability through distribution, parallel CPU capacity, and management of different projects. Finally, it emphasizes that security is a major concern for grid computing due to the open nature of sharing resources across organizational boundaries.
Performance Improvement of Cloud Computing Data Centers Using Energy Efficien...IJAEMSJORNAL
Cloud computing is a technology that provides a platform for the sharing of resources such as software, infrastructure, application and other information. It brings a revolution in Information Technology industry by offering on-demand of resources. Clouds are basically virtualized datacenters and applications offered as services. Data center hosts hundreds or thousands of servers which comprised of software and hardware to respond the client request. A large amount of energy requires to perform the operation.. Cloud Computing is facing lot of challenges like Security of Data, Consumption of energy, Server Consolidation, etc. The research work focuses on the study of task scheduling management in a cloud environment. The main goal is to improve the performance (resource utilization and redeem the consumption of energy) in data centers. Energy-efficient scheduling of workloads helps to redeem the consumption of energy in data centers, thus helps in better usage of resource. This is further reducing operational costs and provides benefits to the clients and also to cloud service provider. In this abstract of paper, the task scheduling in data centers have been compared. Cloudsim a toolkit for modeling and simulation of cloud computing environment has been used to implement and demonstrate the experimental results. The results aimed at analyzing the energy consumed in data centers and shows that by having reduce the consumption of energy the cloud productivity can be improved.
An Efficient Cloud Scheduling Algorithm for the Conservation of Energy throug...IJECEIAES
Method of broadcasting is the well known operation that is used for providing support to different computing protocols in cloud computing. Attaining energy efficiency is one of the prominent challenges, that is quite significant in the scheduling process that is used in cloud computing as, there are fixed limits that have to be met by the system. In this research paper, we are particularly focusing on the cloud server maintenance and scheduling process and to do so, we are using the interactive broadcasting energy efficient computing technique along with the cloud computing server. Additionally, the remote host machines used for cloud services are dissipating more power and with that they are consuming more and more energy. The effect of the power consumption is one of the main factors for determining the cost of the computing resources. With the idea of using the avoidance technology for assigning the data center resources that dynamically depend on the application demands and supports the cloud computing with the optimization of the servers in use.
A Review on Scheduling in Cloud Computingijujournal
This document reviews scheduling techniques in cloud computing. It discusses key concepts like virtualization and different scheduling algorithms. The review surveys various scheduling algorithms for tasks, workflows, real-time applications and energy efficiency. It analyzes algorithms based on parameters like makespan, cost, energy consumption and concludes many algorithms can improve resource utilization and performance while reducing energy costs.
Data Division in Cloud for Secured Data Storage using RSA AlgorithmIRJET Journal
This document proposes a method for secure data storage in the cloud using RSA encryption and data division. User data is first encrypted using RSA encryption. It is then divided into blocks and distributed across multiple cloud servers. Verification tokens are also generated before distribution to allow checking of data integrity stored on the cloud servers. If tokens from the user and cloud servers match, the data integrity is verified. If not, it indicates unauthorized modification of data by someone other than the owner. This approach aims to provide secure storage of user data in the cloud.
Welcome to International Journal of Engineering Research and Development (IJERD)IJERD Editor
This document summarizes research on techniques for virtual machine (VM) scheduling and management to improve energy efficiency in cloud computing. It discusses how VM scheduling algorithms aim to optimally map VMs to physical servers while minimizing costs and power consumption. Precopy and postcopy live migration techniques are described for managing VMs. The document surveys various algorithms for VM scheduling, including ones based on data transfer time, linear programming, and combinatorial optimization. It also discusses factors that affect VM migration efficiency such as hypervisor options and network configuration. Overall, the document provides an overview of energy-efficient approaches for VM scheduling and management in cloud computing.
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
The papers for publication in The International Journal of Engineering& Science are selected through rigorous peer reviews to ensure originality, timeliness, relevance, and readability.
An optimized scientific workflow scheduling in cloud computingDIGVIJAY SHINDE
The document discusses optimizing scientific workflow scheduling in cloud computing. It begins with definitions of workflow and cloud computing. Workflow is a group of repeatable dependent tasks, while cloud computing provides applications and hardware resources over the Internet. There are three cloud service models: SaaS, PaaS, and IaaS. The document explores how to efficiently schedule workflows in the cloud to reduce makespan, cost, and energy consumption. It reviews different scheduling algorithms like FCFS, genetic algorithms, and discusses optimizing objectives like time and cost. The document provides a literature review comparing various workflow scheduling methods and algorithms. It concludes with discussing open issues and directions for future work in optimizing workflow scheduling for cloud computing.
AUTO RESOURCE MANAGEMENT TO ENHANCE RELIABILITY AND ENERGY CONSUMPTION IN HET...IJCNCJournal
This document summarizes an article from the International Journal of Computer Networks & Communications that proposes an Auto Resource Management (ARM) scheme to improve reliability and reduce energy consumption in heterogeneous cloud computing environments. The ARM scheme includes three components: 1) static and dynamic thresholds to detect host over/underutilization, 2) a virtual machine selection policy, and 3) a method to select placement hosts for migrated VMs. It also proposes a Short Prediction Resource Utilization method to improve decision making by considering predicted future utilization along with current utilization. The scheme is tested on a cloud simulator using real workload trace data, and results show it can enhance decision making, reduce energy consumption and SLA violations.
1. Nebula is NASA's open source cloud computing platform built using OpenStack that provides on-demand access to computing resources and storage for large datasets.
2. It allows NASA researchers to run computationally intensive tasks in virtual machines and store huge datasets over 100 terabytes in size.
3. The document discusses Nebula's architecture, services, and case studies of its use at various NASA research centers to support activities like processing images from Mars missions.
An Enhanced trusted Image Storing and Retrieval Framework in Cloud Data Stora...IJERA Editor
Today’s image capturing technologies are producing High Definitional-scale images which are also heavier on memory, which has prompted many users into cloud storage, cloud computing is an service based technology and one of the cloud service is Data Storage as a Service (DSaaS), two parties are involved in this service the Cloud Service Provider and The User, user stores his vital data onto the cloud via internet example: Dropbox. but a bigger question is on trustiness over the CSP by user as user data is stored remote devices which user has no clue about, in such situation CSP has to create a trust worthiness to the costumer or user, in these paper we addressed the mention insecurity issue with a well defined trusted image Storing and retrieval framework (TISR) using compress sensing methodology.
Multicloud Deployment of Computing Clusters for Loosely Coupled Multi Task C...IOSR Journals
This document discusses deploying a computing cluster across multiple cloud providers (Amazon EC2, Elastic Hosts) for loosely coupled multi-task computing applications. It presents an experimental framework using a local data center and three cloud sites. Nine cluster configurations with varying numbers of nodes from each site are evaluated. Performance is analyzed by measuring throughput as jobs/second. Results show hybrid configurations scale linearly and have similar performance to single-site configurations. Cost is also analyzed per job, showing hybrid and local-only configurations have lower cost than cloud-only configurations. A performance-cost analysis indicates for large organizations, a local data center with cloud supplementation can be more cost effective than cloud-only configurations.
This document discusses scheduling in cloud computing. It proposes a priority-based scheduling protocol to improve resource utilization, server performance, and minimize makespan. The protocol assigns priorities to jobs, allocates jobs to processors based on completion time, and processes jobs in parallel queues to efficiently schedule jobs in cloud computing. Future work includes analyzing time complexity and completion times through simulation to validate the protocol's efficiency.
Virtual Machine Allocation Policy in Cloud Computing Environment using CloudSim IJECEIAES
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areas like object recognition, city modelling, virtual reality, physical simulations, video games and
special effects. Previously, to perform a 3D reconstruction, specialized hardwares were required.
Such systems were often very expensive and was only available for industrial or research purpose.
With the rise of the availability of high-quality low cost 3D sensors, it is now possible to design
inexpensive complete 3D scanning systems. The objective of this work was to design an acquisition and
processing system that can perform 3D scanning and reconstruction of objects seamlessly. In addition,
the goal of this work also included making the 3D scanning process fully automated by building and
integrating a turntable alongside the software. This means the user can perform a full 3D scan only by
a press of a few buttons from our dedicated graphical user interface. Three main steps were followed
to go from acquisition of point clouds to the finished reconstructed 3D model. First, our system
acquires point cloud data of a person/object using inexpensive camera sensor. Second, align and
convert the acquired point cloud data into a watertight mesh of good quality. Third, export the
reconstructed model to a 3D printer to obtain a proper 3D print of the model.
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maps. Information from different sources is collected into a central client computer, where point clouds are
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Efficient Point Cloud Pre-processing using The Point Cloud Library
1. Marius Miknis, Ross Davies, Peter Plassmann & Andrew Ware
International Journal of Image Processing (IJIP), Volume (10) : Issue (2) : 2016 63
Efficient Point Cloud Pre-processing using The Point Cloud
Library
Marius Miknis Marius.Miknis@southwales.ac.uk
Faculty of Computing, Engineering and Science
University of South Wales
Pontypridd, CF37 1DL, UK
Ross Davies Ross.Davies@southwales.ac.uk
Faculty of Computing, Engineering and Science
University of South Wales
Pontypridd, CF37 1DL, UK
Peter Plassmann Peter.Plassmann@southwales.ac.uk
Faculty of Computing, Engineering and Science
University of South Wales
Pontypridd, CF37 1DL, UK
Andrew Ware Andrew.Ware@southwales.ac.uk
Faculty of Computing, Engineering and Science
University of South Wales
Pontypridd, CF37 1DL, UK
Abstract
Robotics, video games, environmental mapping and medical are some of the fields that use 3D
data processing. In this paper we propose a novel optimization approach for the open source
Point Cloud Library (PCL) that is frequently used for processing 3D data. Three main aspects of
the PCL are discussed: point cloud creation from disparity of color image pairs; voxel grid
downsample filtering to simplify point clouds; and passthrough filtering to adjust the size of the
point cloud. Additionally, OpenGL shader based rendering is examined. An optimization
technique based on CPU cycle measurement is proposed and applied in order to optimize those
parts of the pre-processing chain where measured performance is slowest. Results show that
with optimized modules the performance of the pre-processing chain has increased 69 fold.
Keywords: Point Cloud, Point Cloud Library, Point Data Pre-processing.
1. INTRODUCTION
Point clouds are sparse spatial representations of 3D object shapes. Algorithms such as the ones
in the frequently used RANSAC [1] method can then be applied to reconstruct the complete
object shapes from the point clouds.
A popular library for storing and manipulating point cloud data is the Point Cloud Library (PCL)
[2]. The PCL is a large scale open source project that is focused on both 2D and 3D point clouds
and includes some image processing functionality. Currently the Library has over 120 developers,
from universities, commercial companies and research institutes. The PCL is released under the
terms of the BSD license, which means that it is free for commercial and research use. It can be
cross compiled for many different platforms including Windows, Linux, Mac OS, Android and iOS.
This allows the library to also be used in embedded systems. The main algorithm groups in the
PCL are for segmentation, registration, feature estimation, surface reconstruction, model fitting,
visualization and filtering.
2. Marius Miknis, Ross Davies, Peter Plassmann & Andrew Ware
International Journal of Image Processing (IJIP), Volume (10) : Issue (2) : 2016 64
In the work presented in this paper stereo-photogrammetry is used as the main method of 3D
data acquisition. This method is based on stereoscopy where two spatially separated images are
obtained from different viewing positions [3]. The analysis of disparity (separation) between
corresponding points in both images encodes the distance of object points which are then stored
in a disparity map.
This paper is organized as follows: section 2 presents related work in the field of 3D data
acquisition and point cloud processing, followed in section 3 by a description of PCL modules and
their optimization, while conclusion and future work are discussed in sections 1 and 1.
2. RELATED WORK
There are many uses for 3D data ranging from environmental perception for robots via
autonomous car navigation, playing video games to medical uses such as wound measurement,
facial reconstruction and more. A number of ways to capture 3D data have been proposed and
implemented. Many existing technologies rely heavily on the use of structured or infrared lighting
to extract the depth data [4]. The technique of structured lighting is widely used in computer vision
for its many benefits [5] in terms of accuracy and ease of use. Over the last 15 years 3D laser
scanners have been developed [6] as active remote sensing devices. Such scanners can quickly
scan thousands or even millions of 3D cloud points in a scene. Time of flight cameras are also
widely used in computer vision. The principle behind these cameras is similar to that of a sonar,
but with light replacing sound. Such cameras were introduced into the wider public domain by the
Microsoft Xbox One console [7] to replace its older structured lighting based Kinect sensor.
Once 3D data has been acquired by the above systems some kind of processing needs to be
applied to extract useful information as well as to remove noise, outliers or any unnecessary
information. With the number of points that can be sampled point clouds can get extremely large
and contain noise as well as outliers and errors. Thus the pre-processing stage is important [8] [9]
as it deals with noise, error and outlier removal through the use of filters as well as smoothing the
point cloud and reducing the point count while still keeping the relevant feature information. There
are software tools available for such processing [10] [11] [12] but very few provide a complete
library framework to incorporate into software projects. 3DReshaper [13] is such a library that
provides point cloud processing capabilities. The PCL is the most commonly used library for point
cloud processing, thus the PCL was used as the main development library in this research.
The current application focus of the PCL library is in the field of robotics. For robots to sense,
compute and interact with objects or whole scenes a way to perceive the world is needed, which
is why the PCL is used as a part of the Robot Operating System (ROS). Using the PCL as a part
of ROS, robots can compute a 3D environment in order to understand it, detect objects and
interact with them. Due to space and power restrictions such systems rarely use desktop-like
computing devices and are therefore in most cases implemented on relatively small embedded
systems. In these systems the universal nature of the PCL (many operating systems, many 3D
data formats, etc.) results in slow performance. The following section III proposes a range of
optimizations in order to improve performance.
3. POINT CLOUD PROCESSING OPTIMISATIONS
Four key algorithm areas were selected for optimization: point cloud creation (section 3.1),
rendering (section 3.2), voxel grid down-sampling (section 3.3), pass through filtering (section
3.4) and the pre-processing chain (section 3.5). For the stereo test data the New Tsukuba Stereo
Dataset [14] was used. This is a collection of synthetic stereo image pairs created using computer
graphics. Additionally, the OpenCV (Open Source Computer Vision Library) was used for image
loading. The project code was run on a desktop Intel i7 machine. The first set of tests used the
Microsoft Visual Studio 2013 code analyzer for inspecting code and its performance statistics.
The purpose of the tests was to identify which parts of the code are using the most of the CPU
calls and then to optimize those.
3. Marius Miknis, Ross Davies, Peter Plassmann & Andrew Ware
International Journal of Image Processing (IJIP), Volume (10) : Issue (2) : 2016 65
3.1. Point Cloud Creation Speed Improvements
When using a stereo camera setup depth values are represented as a disparity map which in
most cases is a greyscale image where the brightness of pixels represents depth values. A
second output is a color image that stores information of the actual color value of the point. From
the disparity and color images a point cloud can be produced. The PCL provides the
OrganisedConversion<>::convert() method which uses the disparity map, color image and the
focal length of the camera to produce a point cloud.
Point cloud generation is in 3 stages: first the input images are loaded into memory using
OpenCV which converts them to vectors that can be passed as parameters to the second stage,
PCL point cloud creation. The point cloud is then rendered on screen in the third stage. Using
Microsoft Visual Studio 2013 code profiler CPU cycles were measured per line of code. In order
to average-out operating system specific random overheads all following test were performed
three times. Results are shown in FIGURE 1.
FIGURE 1: Test figures for CPU usage of different stages. Test 1 – 3 show pre-optimised PCL code,
while tests 4 and 5 show optimised conversion.
• For the first test OpenCV was used to read the Tsukuba dataset as a sequence of images,
loaded one at a time. OpenCV, PCL point cloud generation and rendering algorithms were
used ‘as is’ without changes and as provided from public repositories. The results are
shown in the first bar in FIGURE 1. PCL point cloud generation required 36% of CPU
cycles, rendering 45%. This resulted in a processing speed of 2 frames per second (fps).
• In the second test rendering was disabled to identify CPU load more accurately when
OpenCV loaded images one at a time.
• This is contrasted by the third test where OpenCV loaded images not as individual stills
but as a video sequence. Encoding the still images into a video sequence was achieved
using the OpenCV Intel IYUV. This had a dramatic effect as OpenCV CPU cycles reduced
from 27% to only 3%, leaving the remaining almost 97% to the PCL conversion.
• In order to improve PCL performance numerous optimizations were made. In particular,
these were a) bit-shifting pointer incrementation of color values to allow faster access and
modification of values, b) vector clear and resize checks to avoid clearing and resizing a
new vector when it is the same size as the previous one c) vector access optimizations
through the use of data pointers which allowed the optimization of vector pushback
overhead and d) several minor optimizations. The source code and documentation of
these changes are available in the PCL developer’s forum [15]. The 4
th
bar in FIGURE 1
shows that as a result the CPU cycles needed for PCL conversion reduced by 66% to less
than the cycles needed for image loading by OpenCV.
• The two improvements documented in tests 3 and 4 were finally tested in the same way
as in the first test of this series, i.e. with rendering switched on again. With image loading
replaced by video loading and conversion optimized the total cycle usage of these two
2fps n/a n/a n/a 5fps
4. Marius Miknis, Ross Davies, Peter Plassmann & Andrew Ware
International Journal of Image Processing (IJIP), Volume (10) : Issue (2) : 2016 66
components now consume less than 10% of processor cycles while rendering now takes
72%. Importantly, the overall frame rate increased to 5 frames per second.
3.2. Rendering Speed Improvements
Since rendering was now the new bottleneck, steps were taken to improve its performance.
By default, rendering for the PCL is done by The Visualization Toolkit (VTK) which is an open
source library for 3D computer graphics and image processing. This was replaced with a shader
(i.e. graphics processor) based OpenGL rendering implementation for desktop PCs.
The basic data structure inside the PCL is the point cloud. This is an assembly of sub-fields. The
main ones are ‘width’, ‘height’ and ‘points’. ‘Points’ is a vector that stores points of PointT type
which in turn can be PointXYZ, PointRGB, PointRGBA (and some other basic types). Under the
existing PCL data structure non-colored point clouds of type PointXYZ could be rendered with our
new OpenGL implementation but not colored ones. To enable this several changes were made to
the PCL:
• A fourth float value was added to the point cloud type union. This was easy to do since the
union already had memory allocated for four float values but only x, y and z floats were
declared. The forth parameter added now stores the color value to be passed to the
OpenGL shaders.
• To store the color values the three constituent independent integer values were bit-shifted
into a single float which was then stored as the fourth value of the above union. This was
done to avoid integer calculations having to be performed in the shaders while at the
same time having minimal impact on the PCL.
• However, OpenGL shaders do not support bit shifting. The color values were therefore
extracted in the shader by manipulating the known structure (8 bits for each of the
channel). In the vertex shader the floor() method was used to extract each color channel
separately as the return value is an integer.
The result of the above manipulations are shown in FIGURE 2. The two bars labelled ‘VTK’ are
unchanged re-runs of the first and fifth group tests from the previous section (see FIGURE 1).
When in the first test VTK is replaced by OpenGL the frame rate increases by a modest 50% to 3
fps. When, however, this is done in the optimized system produced in the previous section the
speed improvement is considerable: 38 fps. In this final system where all three components are
optimized, OpenGL rendering uses only 8.5% of the processor cycles while before VTK used up
72%.
FIGURE 2: 1st and 5th re-tests (using the standard VTK renderer) compared to new OpenGL renderer.
The first 2 bars represent performance of the non-optimised PCL code and the 3rd and 4th bar the optimised
PCL/OpenCV code.
2fps 3fps 5fps 38fps
5. Marius Miknis, Ross Davies, Peter Plassmann & Andrew Ware
International Journal of Image Processing (IJIP), Volume (10) : Issue (2) : 2016 67
3.3. Voxel Grid Downsample Filter Improvements
After the point cloud has been produced further processing is usually required, e.g. for data
reduction and filtering operations. A relatively low resolution point cloud of 640 x 480 (e.g.
produced by the Kinect) results in 307,200 points. While for some operations (e.g. thresholding)
point processing follows an O(n) notation a more complex algorithm (e.g. k nearest neighbor
filtering) becomes O(nk). This can place a heavy workload on the processor.
One of the methods frequently used to lower the amount of points in a point cloud and
unnecessary complexity while retaining detail and information is voxel grid down sampling. The
down sampling is performed using an octree to sub-divide the point cloud into multiple cube
shaped regions (voxels). After processing, all points in the voxel are reduced to a single one. This
results in a point cloud that is smaller in size and complexity but is still precise enough to work
with and has a smaller cost in terms of CPU performance. The PCL has a dedicated method for
this called voxelGrid.filter(). For testing the leaf size values of the filter were 0.03f, 0.03f, 0.03f
(3x3x3cm). Three groups of tests were performed as shown in FIGURE 3.
FIGURE 3: Test figures for CPU usage of voxel grid. Test 1 shows the stock code, test 2 shows results
with Quicksort algorithm implemented and test 3 shows overall optimised voxel grid performance.
• In the first group of tests voxel filtering was added to the optimized processing chain
developed in the previous two sections A and B. Voxel grid computation proved to be very
CPU intensive with overall CPU cycle usage of 98%. This also resulted in a poor frame
rate of under 0.1 fps (8.6 seconds per frame). Analysis of the filter code revealed that 30%
of the processing was spent on sorting the points using a standard C++ library vector sort
method.
• The second group of tests was therefore performed with the sort method replaced by a
Quicksort algorithm [16]. This algorithm takes on average O(n log n) steps to sort n points,
but in the worst case scenario when a chosen pivot value is the smallest or largest of the
points to sort the algorithm has to make O(n
2
) comparisons. To avoid this possible issue a
mean value is computed before the sorting to avoid using very small or very large values
as the pivot. Compared to the standard C++ sort with 30% of processor cycles used,
Quicksort was significantly more efficient, using only 0.9%. This unfortunately improved
the overall filter method by only 5.2% as the computation shifted to different parts of the
algorithm, mostly to vector access overheads.
• For the third test group vector access was therefore optimized by replacing vector
pushback calls with pointer accesses and improving the centroid finding which together
took up 65% of the processing. These changes reduced the voxel filter computation time
by 26% to an overall contribution of that in total using only 72% of CPU cycles.
The combined changes to the sorting and vector processes increased the frame rate 91-fold to an
average frame rate of about 10 fps.
0.1fps 0.1fps 10fps
6. Marius Miknis, Ross Davies, Peter Plassmann & Andrew Ware
International Journal of Image Processing (IJIP), Volume (10) : Issue (2) : 2016 68
3.4. Pass Through Filter Improvements
Another PCL provided post-processing method is passthrough.filter() which is as a means to
allow the removal of points from the cloud which are not within a specified range. This allows the
point cloud to be adjusted in any coordinate direction similar to a frustum cut-off. The
passthrough.filter() method accepts parameters for upper and lower limits and a direction along
the x, y or z axis. For the Tsukuba dataset the depth range values of 3 and 12 were used for
testing in the z coordinate direction. Two groups of tests were performed with results shown in
FIGURE 4.
FIGURE 4: Test figures for CPU usage of pass through filter. The first and second test showing the stock
code performance and second improved code performance respectively.
• In the first test the pass through filter was appended to the optimized processing chain
outlined previously in sections A and B. The filter was very CPU intensive using 93.6% of
cycles bringing down the frame rate to 3 fps. Analysis of the code showed that (as before
with voxel filtering) vector accesses were inefficient.
• After vector access optimization along the lines outlined before with voxel filtering and
improving the non-finite entries check (54%) as well and field value memory copy calls
(24%) the pass through filter now only consumes 41% of CPU cycles with the frame rate
rising to 18 fps.
3.5. Combined Pre-processing Chain
The PCL modules analyzed above when combined create the main pre-processing chain of the
point cloud manipulation. The order in which these algorithms are applied makes a substantial
performance difference.
Running the voxel filter first proved to be the slower combination as the down sampling had to be
performed on the whole point cloud, in this case 307,200 points. Looking at FIGURE 5 it can be
seen that voxel grid computation is the most CPU intensive task taking up 92% of all processing.
Pass through filtering only took up 2% of CPU cycles and organized PCL conversion 4%. An
optimized version saw a more balanced use of the processing with voxel grid processing lowered
to 68% and pass through filtering at 12%. Organized conversion rose to 10% and OpenCV’s
contribution increased to 7% from 0.2% previously.
3fps 18fps
7. Marius Miknis, Ross Davies, Peter Plassmann & Andrew Ware
International Journal of Image Processing (IJIP), Volume (10) : Issue (2) : 2016 69
FIGURE 5: CPU usage shown for pre-processing applying voxel grid filter computations before pass
through filtering.
When the pass through filter was applied first the performance changed by a great margin. As
shown in FIGURE 6 it can be seen that the voxel grid process is still the most CPU intensive part
but has improved over the previous order. It was found to only use 65% of processing steps
instead of 92%. This led to CPU cycles being distributed more evenly between the pass through
filter (13%) and organized PCL conversion (20%). The optimized version of the modules exhibits
the most even distribution of processing with voxel grid contribution lowered to 21% and pass
through filtering taking up 33% of CPU cycles. Organized conversion used up 24% and OpenCV
18% respectively.
FIGURE 6: CPU usage shown for pre-processing applying pass through filtering before voxel grid
computations.
The order of code execution has led to a significant change in performance (see FIGURE 7).
When the voxel grid was processed before the pass through filter the stock code was not able to
render more than 0.1 fps, i.e. it took around 9.1 seconds to render a single frame. This order
when used with the optimized code has shown a significant improvement as the frame rate
increased to 3fps, i.e. it only took 98 milliseconds on average to render a single frame, making it
on average up to 93 times faster. Similar results were seen in the reverse arrangement. The
stock code with pass through filtering being applied first was able to render 0.4 fps (2.5 seconds
per frame) which is a four times better performance. The biggest change was seen in the overall
optimised code frame rate which on average was 25 fps making it close to real time performance
0.1fps 9fps
0.4fps 25fps
8. Marius Miknis, Ross Davies, Peter Plassmann & Andrew Ware
International Journal of Image Processing (IJIP), Volume (10) : Issue (2) : 2016 70
as it only took 37 miliseconds to render a frame. Overall this is a 69 times better performance
compared to the original unaltered stock code.
FIGURE 7: Frame rates shown of stock and optimised modules in different execution orders.
To further support and test the findings additional testing was performed on a wide range of
devices which included embedded systems such as Raspberry Pi 1 and 2, tablets, laptops and
powerful rendering machines. In total eighteen different machines were used to perform a
comparative evaluation between the stock and optimised code, of which some ran a Linux
operating system to give a full spectrum of hardware and software combinations. These results
show that optimised code was able to increase the performance for every single machine tested.
The embedded systems saw the smallest increase due to their lack of power on the ARM based
processor, but still saw four times better performance with optimise code compared to stock. As
the power of machines increased so did the optimised code performance while stock stayed
almost level.
FIGURE 8: Comparative evaluation test results between stock and optimised code on eighteen different
machines sorted from least powerful(left) to most powerful(right).
9. Marius Miknis, Ross Davies, Peter Plassmann & Andrew Ware
International Journal of Image Processing (IJIP), Volume (10) : Issue (2) : 2016 71
4. CONCLUSIONS
Since the PCL is a general purpose and multi-platform library many of its internal aspects are
generalized, not all parts are optimized and performance can suffer on time sensitive processing.
As shown in section 3 optimized PCL modules provide significant performance gains over the
stock modules. When neglecting the minimal cost of performance testing measurement
overheads speed increased 2.4 times for the organized PCL conversion, 91 times for voxel grid
filtering and 7.8 times for pass through filtering. As seen in section 3.5 this allows for the use of
multiple PCL modules together while still maintaining near real-time frame rates giving an
average of 69 times improved performance for the pre-processing of the point clouds. It is
important to note that the optimized code is still generalized, not specific to a particular platform
and backwards compatible with existing stock code. The optimized modules in this paper have
not been changed since libraries release 2011 showing the need for the update and
improvement. The point cloud pre-processing optimizations are important for various point cloud
tasks such as registration, object recognition and segmentation. Part of these improvements are
already being implemented to the library project by the community.
5. FUTURE WORK
Future plans focus on working with PCL developer community, and to contribute optimized
algorithms to the official PCL code repository. Another part of research has already been started
to allow the PCL to be used with embedded devices to perform real time point cloud processing.
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